The Ledford and Tawn model for the bivariate tail incorporates a coefficient, η, as a measure of pre-asymptotic dependence between the marginals. However, in the limiting bivariate extreme value model, G, of suitably normalized component-wise maxima, it is just a shape parameter without reflecting any description of the dependency in G. Under some local dependence conditions, we consider an index that describes the pre-asymptotic dependence in this context. We analyze some particular cases considered in the literature and illustrate with examples. A small discussion on inference is presented at the end.

Extreme value modeling has been attracting the attention of researchers in diverse areas such as the environment, engineering, and finance. Multivariate extreme value distributions are particularly suitable to model the tails of multidimensional phenomena. The analysis of the dependence among multivariate maxima is useful to evaluate risk. Here we present new multivariate extreme value models, as well as, coefficients to assess multivariate extremal dependence.

We present a method, that uses pivot variables, which are functions of statistics and parameters, of constructing confidence intervals for variance components in gauge capability studies. As illustration we will consider a study on repeatability and reproducibility measures. Besides this the paper includes a simulation study demonstrating that in approximately 9500 out of 10000 simulations the 95% confidence interval covers the true value of the parameter.

O software R ´e uma ferramenta extremamente u´til para a investiga¸c˜ao estat´ıstica. No entanto, a prolifera¸c˜ao de bibliotecas (na ordem dos milhares) dificulta o r´apido e eficiente acesso a todas as possibilidades em cada uma das ´areas desta ciˆencia. Uma forma de limitar esta procura ´e aceder `a task view correspondente, se existir. Pelos motivos descritos, neste trabalho procura-se compilar informac¸˜ao relevante nas ´areas de an´alise de sobrevivˆencia e de valores extremos, de modo a minimizar as dificuldades referidas. A abordagem na an´alise de sobrevivˆencia, que possui task view, ser´a sobretudo atrav´es de exemplos. Nos valores extremos ser´a dada uma vis˜ao geral do que existe, uma vez que nesta ´area n˜ao h´a task view.

Na modela¸c˜ao de acontecimentos m´ultiplos, uma abor dagem muita utilizada consiste em desenvolver extens˜oes do modelo semiparam´etrico de Cox. Contudo, quando se considera que o co nhecimento da distribui¸c˜ao do tempo ´e importante para o estudo, a abordagem param´etrica revela-se mais adequada. Neste trabalho, s˜ao apresentados dois novos modelos param´etricos baseados na dis tribui¸c˜ao de Weibull (modelos WNE e WE), como alternativas a duas das extens˜oes do modelo de Cox para acontecimentos recorren tes (modelos AG e PWP). De forma a ilustrar as diferen¸cas entre estas duas abordagens, ´e considerado um exemplo de aplica¸c˜ao com dados simulados. Os resultados obtidos com os modelos propostos permitem acrescentar mais duas op¸c˜oes ao leque de escolhas de mo delos para acontecimentos recorrentes.

Systemic risk is a specific type of risk that refers to the risk of an complex system to be affect or even collapse due to individual action taken by the agents that compounds that complex system. The goals of this work is based on an axiomatic approach establish a critic description of the most relevant methods used in the determination of systemic risk and identify advantages and disadvantages associated to those methods.

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model.